An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework

Autor:	Sezer, O. B., Ozbayoglu, M., Dogdu, E.
Rok vydání:	2017
Předmět:	Computer Science - Computational Engineering Finance and Science Quantitative Finance - Trading and Market Microstructure Statistics - Machine Learning 68TXX
Zdroj:	ACM Southeast Conference, ACMSE 2017, Kennesaw State University, GA, U.S.A., 13-15 April, 2017
Druh dokumentu:	Working Paper
DOI:	10.1145/3077286.3077294
Popis:	In this paper, a neural network-based stock price prediction and trading system using technical analysis indicators is presented. The model developed first converts the financial time series data into a series of buy-sell-hold trigger signals using the most commonly preferred technical analysis indicators. Then, a Multilayer Perceptron (MLP) artificial neural network (ANN) model is trained in the learning stage on the daily stock prices between 1997 and 2007 for all of the Dow30 stocks. Apache Spark big data framework is used in the training stage. The trained model is then tested with data from 2007 to 2017. The results indicate that by choosing the most appropriate technical indicators, the neural network model can achieve comparable results against the Buy and Hold strategy in most of the cases. Furthermore, fine tuning the technical indicators and/or optimization strategy can enhance the overall trading performance. Comment: ACM Southeast Conference, ACMSE 2017, Kennesaw State University, GA, U.S.A., 13-15 April, 2017
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1712.09592 Zobrazit plný text záznamu View this record from Arxiv